جستجوی استاد راهنمای دانشگاهی

Francis Wyffels is an early career professor at Ghent University, affiliated with the Internet Technology and Data Science Lab (IDLab). His expertise spans machine learning, robotics, and computer science education, with significant contributions to European research projects like FP7 AMARSi on robot control. Wyffels' research focuses on robot control, embodied cognition, and (deep) reinforcement learning, applied to agricultural robotics, cloth manipulation, and human-robot interaction. He actively promotes realistic public perspectives on robotics through educational initiatives, countering societal fears about automation. His work bridges theoretical neural networks with practical applications in precision agriculture and educational tools. Recent publications (2024-2025) reveal three dominant trends: agricultural robotics (e.g., intrarow weeding, crop monitoring), deformable object manipulation (e.g., cloth handling), and computational thinking education. These reflect his commitment to solving real-world problems while advancing robotic capabilities in unstructured environments. Scientific awards received: UGent Hermes award for public/scientific engagement and knowledge transfer Google RISE award Wyffels leads research within IDLab, coordinating robot-related work packages in international projects. His approach combines technical innovation with public dissemination, emphasizing robotics' societal benefits in productivity and convenience. Current projects include agricultural automation frameworks and emotion-conveying social robots. He directs the Internet Technology and Data Science Lab (IDLab), where his team develops systems for precision farming, robotic manipulation, and educational tools. Recent work includes DIY social robot platforms and compliant control systems for soft-body insertion, demonstrating IDLab's focus on accessible, real-world robotics solutions.

• Robot control
• Embodied cognition
• Reinforcement learning
• +۵ مورد دیگر

Pierre-Yves Gousenbourger is a Teaching Assistant at the École polytechnique de Louvain (Louvain School of Engineering), part of the Catholic University of Louvain. He is affiliated with the ICTEAM research institute and supervises students in the Department of Electrical Engineering. His pedagogical approach includes the Apprentissage Par Projet (project-based learning) methodology. He holds a PhD (2020) and a Master's in Applied Mathematics from UCLouvain, both supervised by Pierre-Antoine Absil. His doctoral thesis, Interpolation and fitting on Riemannian manifolds , developed efficient algorithms for manifold-valued data fitting with applications in UAV control, medical imaging, and model reduction. His research focuses on interpolation, optimization, and imaging on Riemannian manifolds , emphasizing Bézier curves/surfaces for data approximation. Key areas include wind field estimation via covariance matrix fitting on SPSD manifolds, medical shape reconstruction (e.g., endometrial surfaces), and parametric model order reduction using manifold interpolation. His work integrates Riemannian geometry with computational efficiency, leveraging tools like exponential/logarithm maps. His publications consistently address data fitting on nonlinear manifolds , featuring Bézier-based methods, variational formulations, and applications spanning computer vision, medical imaging, and aerodynamics. Trends include denoising corrupted data, accelerating computational workflows, and generalizing Euclidean splines to manifolds. He collaborates with the RANSO and Dysco research groups and has worked with institutions including MIT, TU Chemnitz, and the University of Münster. He develops open-source numerical tools for manifold interpolation, distributed under the GNU GPL license.

• Interpolation on Manifolds
• Optimization on Manifolds
• Bézier Curves and Surfaces
• +۵ مورد دیگر

Sam Leroux is a Tenure Track Assistant Professor and IMEC Postdoctoral Researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology. His research spans multiple interdisciplinary domains where artificial intelligence meets real-world applications, with particular emphasis on machine learning, deep neural networks, and distributed systems. He maintains active collaborations with IMEC and contributes to several EU and national research initiatives focused on practical AI deployment. Dr. Leroux's research interests center on developing efficient and privacy-aware machine learning systems that can operate effectively on resource-constrained devices. His work bridges theoretical advances in neural network architectures with practical applications in agriculture, healthcare monitoring, industrial IoT, and sustainable computing. He has pioneered approaches in adaptive neural networks that dynamically adjust computation based on available resources, enabling AI deployment at the network edge without compromising privacy. His publication record reveals a clear trajectory from foundational work in neural network architectures toward increasingly applied research. Recent publications demonstrate strong focus on privacy-preserving AI techniques, hardware-efficient machine learning, and computer vision applications in agriculture and industrial settings. His work consistently addresses the tension between model performance and resource constraints, with growing emphasis on ethical considerations in AI deployment. As an academic supervisor, Leroux currently guides numerous doctoral researchers across diverse projects including broiler welfare monitoring, hardware-efficient continuous learning, UAV-based agricultural sensing, and privacy-aware ergonomic analysis. He serves as Promotor for the 'Hardware-efficient continuous learning' project funded by the Special Research Fund, demonstrating his leadership in securing competitive research funding. His work environment features strong connections between Ghent University's academic research and IMEC's technological expertise, creating a fertile ground for translating theoretical advances into practical solutions. This positioning enables his research group to tackle challenges spanning from algorithm development to hardware implementation, with particular focus on real-world validation of proposed techniques.

• Machine Learning
• Deep Learning
• Neural Networks
• +۵ مورد دیگر

Alexandru-Cristian Mara is a postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Electronics and Information Systems . His research focuses on network representation learning, graph embeddings, and machine learning methodologies. Research Interests : Network embedding evaluation, link prediction, graph robustness, and reproducibility in machine learning. Methodological Contributions : Developed EvalNE , a framework for evaluating network embeddings on link prediction tasks. Key Findings : Demonstrated limited progress in network embedding methods compared to simple heuristics and analyzed robustness against network perturbations.

• Computer Engineering
• Mathematical Engineering
• Graph Algorithms
• +۵ مورد دیگر

Vincent Englebert is a Professor at the University of Namur's Faculty of Computer Science, where he is affiliated with the Research Center on Information Systems Engineering (PReCISE) and the Namur Digital Institute (NADI). His office is located in the IT Building at Rue Grandgagnage, 21 in Namur, Belgium. He teaches courses including Organisational and Business Modelling, Software Architecture Engineering, and Software Engineering Laboratory for the 2023-2026 academic period. Bachelor and Master in Computer Science (University of Namur) Doctor of Science in Computer Science (2000), thesis: 'A smart Meta-CASE: towards an integrated solution' Englebert's research focuses on Domain-Specific Modeling Language (DSML) Engineering, Modeling & Meta-modeling, and Software Architecture Engineering, with additional interest in Computers and Aging applications. His work spans theoretical foundations of modeling languages to practical applications in business processes and aging populations. The fingerprint of his research shows strong emphasis on design (100%), engineering (78%), computer codes (70%), Reverse Engineering (63%), architecture (60%), tools (56%), modeling (55%), and transformations (50%). His recent publications (2020-2024) demonstrate continued productivity in model-driven engineering, with particular focus on software product lines, feature modeling, and applications for aging populations. These works show a clear trajectory from theoretical modeling foundations to practical implementations and societal applications. Best paper at International Conference Modelsward'2014 Englebert has supervised numerous research projects, including the current e-Anacarde: Digitalisation project (2024-2028) and previously led major initiatives like UsiXML and METADONE. He has been active in media and public engagement, particularly regarding computer science education in schools, with multiple expert commentaries in 2016. His external responsibilities have included significant leadership positions, notably as Dean of the Faculty of Computer Science (2015-2019) and Director of the PRECISE/IS research center (until 2011). He is active in research networks and collaborations, with recent external collaborations spanning multiple countries. His work with the NAM-IP Museum demonstrates ongoing engagement with cultural institutions, while his presentations on Alzheimer's disease and computer technology show commitment to applying informatics to societal challenges.

• Domain-Specific Modeling Language Engineering
• Modeling & Meta-modeling
• Software Architecture Engineering
• +۴ مورد دیگر

Johan Wagemans is a full professor at the Faculty of Psychology and Educational Sciences, KU Leuven, leading the Laboratory for Experimental Psychology. He actively contributes to DigiSoc, LBI, and Leuven.AI institutes. Research Focus: Interdisciplinary work at the intersection of visual perception, computational aesthetics, and AI-mediated art. Key themes include perceptual organization, mid-level vision, and the application of machine learning to aesthetic assessment. Current projects cover symmetry detection, cross-cultural aesthetics, AI-generated art, and neural modeling of perceptual processes Recent publications emphasize computational tools, dataset development, and Gestalt principles in modern perception research Methodological Innovations: Pioneering techniques in deep learning, psychophysics, and naturalistic shape analysis to bridge cognitive science and technological applications.

• Visual Perception
• Cognitive Psychology
• Computational Aesthetics
• +۳ مورد دیگر

Eugen Pircalabelu is a Lecturer at UCLouvain (Université catholique de Louvain) working at the Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) within LIDAM at the Faculty of Science. Prior to his current position, he held a Visiting Professor position at Ghent University and a Postdoctoral position at KU Leuven. Dr. Pircalabelu's research focuses on high-dimensional statistics, with particular emphasis on probabilistic graphical models, social network analysis, copula models, and information criteria. His work bridges theoretical statistics with practical applications in fields such as neuroscience (fMRI data analysis) and epidemiology (COVID-19 modeling). His recent publications reveal a strong trend toward distributed and federated learning approaches for high-dimensional graphical models, with applications spanning from financial time series to brain connectivity networks. His methodological contributions include innovations in model selection criteria, sparse estimation techniques, and time-varying network models that have advanced the field of high-dimensional statistical inference. Dr. Pircalabelu actively supervises multiple PhD students including Mengxue Li, Lise Léonard, and Lara Wautier, and has recently guided Ensiyeh Nezakati and Alexandre Jacquemain to completion of their doctoral studies. He teaches courses in nonparametric statistics, numerical methods for statistics, and statistical learning. He serves the statistical community through organizing regular Statistical/Econometrics seminars at ISBA and contributing to the RShiny@UCLouvain platform for open educational resources. Notably, during the COVID-19 pandemic, he developed a SHINY app for predicting hospitalizations and ICU admissions in Belgium.

• High-dimensional statistics
• Probabilistic graphical models
• Social network models
• +۶ مورد دیگر

Nikolaos Deligiannis is an Associate Professor at the Department of Electronics and Informatics (ETRO) of Vrije Universiteit Brussel (VUB). He holds a PhD in Engineering Sciences from VUB (2012) and a Diploma in Electrical & Computer Engineering from the University of Patras (2006). Previously, he was a Senior Researcher at University College London (2013-2015). His research focuses on interpretable machine learning, federated AI, and applications in image/video processing, computer vision, and natural language processing. He is an IEEE member and serves as an Associate Editor for the IEEE Transactions on Image Processing. He leads major projects such as the EU-funded 'Reinventing Multiterminal Coding for Intelligent Machines' (2025-2030) and 'ENACT: Environmental Effect on Health Care' (2025-2028). His work spans 202+ publications, including seminal papers on explainable AI and federated learning. Key awards include the EURASIP Best PhD Award (2017) and the Francqui Research Professorship (2024). Deligiannis has supervised 39 student theses, including master's works on distributed query systems, algorithmic trading, and water level forecasting. His research also addresses healthcare applications, such as developing a prognostic model for COVID-19 severity using thoracic CT scans. He actively participates in conferences and workshops, delivering talks on topics like graph-based explanations and deep learning optimization.

• Interpretable Machine Learning
• Explainable AI
• Federated AI
• +۵ مورد دیگر

Jan Lemeire is an active researcher at Vrije Universiteit Brussel (VUB), affiliated with the Department of Electronics and Informatics within the Faculty of Engineering. Based in Brussels, Belgium at Pleinlaan 2, he maintains an active research profile with an h-index of 10 and 581 citations according to Scopus data. His research interests span multiple domains including GPU computing, machine learning, embedded systems, and their applications in diverse fields from biomedical engineering to forensic science. His work demonstrates strong interdisciplinary connections, bridging computer science with practical applications in health technology, crime analysis, and industrial systems. Analysis of his recent publications reveals a consistent focus on computational efficiency, with particular emphasis on GPU acceleration, embedded AI deployment, and machine learning applications. His work shows evolution from hardware-focused research toward more applied domains including healthcare technology and crime pattern analysis. Dr. Lemeire actively participates in multiple research projects including NSIS2: PRISMA network (2024-2029), IOF3016: GEAR (2021-2025), and Tech4Health (2024-2025), demonstrating sustained research funding and collaborative work across disciplines. His academic activities include supervision of graduate students, as evidenced by his role as advisor for the 2017 Master's thesis on GPU-accelerated holography, and regular participation in major conferences including the Conference on Uncertainty in Artificial Intelligence and the International Symposium on Embedded Multicore Systems. Dr. Lemeire maintains an active research laboratory focused on computational methods, with particular strengths in parallel processing techniques and their application to real-world problems across multiple domains including healthcare, industrial systems, and forensic science.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

David Blinder is a Professor at the Vrije Universiteit Brussel (VUB) in the Department of Electronics and Informatics (ETRO), where he maintains a dual affiliation with the imec research institute. He concurrently holds a JSPS International Research Fellow position at Chiba University, leveraging over a decade of cross-institutional expertise in optics, signal processing, and computer science to advance computational holography and photonics. His research program centers on signal processing in photonics and holography, with emphasis on numerical diffraction, time-frequency analysis, data compression, and computational imaging. As lead editor of the JPEG Pleno Holography standard, he drives innovation in holographic data representation and transmission. His work integrates optical setups, holographic displays, and high-performance computing to solve complex problems in 3D visualization and inverse methods. Recent publications demonstrate a cohesive trajectory toward efficient computational frameworks for holographic systems, with recurring themes in numerical diffraction algorithms, polygon-based hologram generation, and asymmetric point-spread function optimization. These advances directly enable next-generation holographic displays and computational imaging applications across medical visualization and augmented reality. Blinder's contributions have been recognized through significant honors: Editor's Pick award (October 2024) Editor's Pick award (September 2024) Suzuki-Okada Memorial prize (June 2024) OSA Outstanding Reviewer recognition (2018) He has mentored master's students including A. Ouahabi (2015, Motion Estimation for Dynamic Holography) and F. Suarez Groen (2017, GPU-optimized Computer-Generated Holography). His research is sustained by major grants: FWOAL1100: Advanced dynamic computer-generated holography (2024-2026) FWOAL1101: Computational Incoherent holographic plenoptic camera (2024-2027) FWOTM1099: Chirplet-based diffraction framework (2022-2027) Tech4Health: Future health technologies (2024-2025) As a core member of VUB's ETRO department and imec, Blinder leads interdisciplinary teams in projects like GEAR and Tech4Health, developing holographic visualization systems for medical applications while advancing fundamental optical computing methodologies.

• Optics
• Signal Processing
• Computer Science
• +۷ مورد دیگر

Adrian Munteanu is a Professor in the Department of Electronics and Computer Science at Free University of Brussels (Vrije Universiteit Brussel). He has been actively contributing to research in image processing, deep learning, and signal processing since 1995, with over 414 research outputs and an h-index of 29. His work spans diverse fields such as video compression, autonomous vehicle pose estimation, greenhouse gas emissions prediction, and real-time distributed video coding.

• Image Processing
• Deep Learning
• Signal Processing
• +۶ مورد دیگر

Professor Anne-Mieke Vandamme is a Full Professor at the KU Leuven Faculty of Medicine, affiliated with the Department of Microbiology, Immunology and Transplantation. She leads the Laboratory of Clinical and Epidemiological Virology at the Rega Institute and holds memberships in the DigiSoc Digital Society Institute, Leuven One Health Institute, and LIVO Educational Research Institute. Her research focuses on virology, transdisciplinary education, and global health challenges like HIV drug resistance. She supervises multiple PhD projects and has authored impactful studies in Computerized Medical Imaging and Graphics , Global Health Research and Policy , and Archives of Virology . Key roles include promoting EU health strategies, advancing virus taxonomy, and designing transdisciplinary innovation labs in higher education. Education background: Not explicitly detailed in provided text Research interests emphasize virus-triggered malignancies, public health surveillance, and transdisciplinary knowledge co-creation. Recent projects include the WILLEM surveillance effectiveness initiative and the HIVDR-Prevent Tanzania intervention design. She co-authored a 2024 rapid review on inclusive pandemic communication strategies during crises. Advising involves supervision of PhD candidates like G.M. Bwire and leadership in over 10 research projects as Promotor/Co-promotor. Her labs and teams focus on pathogen surveillance literacy (EpiQuest project) and pandemic preparedness education.

• Virology
• Infectious Diseases
• Public Health
• +۳ مورد دیگر

Aleksandra Pizurica is a Professor in statistical image modelling at Ghent University, Belgium, affiliated with the Group for Artificial Intelligence and Sparse Modelling (GAIM). She serves as Senior Area Editor for IEEE Transactions on Image Processing (2016–) and Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology (2016–), having previously held editorial roles at IEEE Transactions on Image Processing (2012–2016). Her educational background includes: Dipl. Ing. in Electrical Engineering, University of Novi Sad (1994) MSc in Telecommunications, University of Belgrade (1997) PhD in Engineering, Ghent University (2002) Prof. Pizurica's research centers on statistical modelling , probabilistic graphical models , and Bayesian inference , with significant contributions to sparse coding , signal/image processing , and machine learning . Her work bridges theoretical advances with applications in medical imaging, remote sensing, and cultural heritage preservation, particularly in image denoising, inpainting, and hyperspectral analysis. Analysis of her 15 most recent publications (2023–2025) reveals dominant themes in hyperspectral image processing (clustering/classification via model-aware deep learning), medical imaging (3D foot/ankle alignment, musculoskeletal segmentation), and cultural heritage (crack detection in paintings). Emerging trends include fairness in AI (skin color bias mitigation), scalable seabed mapping, and generative models for point cloud processing. Her notable recognition includes: Scientific Prize “de Boelpaepe” for 2013-2014 from the Royal Academy of Science, Letters and Fine Arts of Belgium While specific grant details and student advisement records aren't provided in available sources, her editorial leadership and research output indicate active supervision of graduate researchers. She leads initiatives in the Group for Artificial Intelligence and Sparse Modelling (GAIM), focusing on statistical image modeling and sparse representations for real-world applications. The GAIM research unit under her affiliation drives innovation in probabilistic modeling and machine learning, with projects spanning medical diagnostics, remote sensing, and digital art restoration, evidenced by recent publications in IEEE Transactions and high-impact journals.

• Statistical modelling
• Probabilistic Graphical Models
• Bayesian inference
• +۵ مورد دیگر

Peter Veelaert is a Professor in the Department of Telecommunication and Information Processing at Ghent University, Belgium. He was born in Mortsel in 1958 and earned both his M.S. and Ph.D. in electrical engineering from Ghent University in 1981 and 1992. His research focuses on computer vision, sensor fusion, and intelligent vehicles, with a particular emphasis on digital geometry and embedded systems applications. Education: M.S. (1981) and Ph.D. (1992) in Electrical Engineering from Ghent University Veelaert teaches courses in computer vision, multimedia, algorithms on embedded systems, and digital design. His research unit, Image Processing and Interpretation (IPI), investigates geometric constructions, multi-target tracking, and behavior analysis through sensor fusion techniques. Key publication trends include advancements in digital planarity, geometric primitives, and robotics obstacle avoidance systems. Contact: Email Peter.Veelaert@UGent.be | Publications

• Computer Vision
• Sensor Fusion
• Intelligent Vehicles
• +۶ مورد دیگر

Lionel Maes is affiliated with the Ecole de Recherche Graphique (ERG), where he teaches courses in digital imaging, visual communication, and coding. He holds the position of Lecturer in the Department of Digital Arts and Visual Communication. Prior to his academic role, he worked as a web developer, graphic designer, and developer, and co-founded the creative enterprise La Villa Hermosa. He graduated from ERG with a specialization in digital arts and visual communication. Education: Bachelor/Master in Digital Arts and Visual Communication, Ecole de Recherche Graphique His professional experience spans both academic instruction and creative industries, focusing on blending technical skills (coding, web development) with artistic disciplines (graphic design, digital imaging). While no specific research grants or lab affiliations are mentioned, his teaching and industry projects reflect expertise in digital media and design education.

• Digital Art
• Graphic Design
• Web Development
• +۳ مورد دیگر